Development of a standard set of outcome measures for non-specific low back pain in Dutch primary care physiotherapy practices: a Delphi study
Bibliographic record
Abstract
PURPOSE: To develop a clinical standard set of outcome measures that are accepted for relevance and feasibility by stakeholders and useful for (a) interaction between patient and the professional, e.g. shared decision-making in goal-setting, monitoring and feedback based on outcomes, (b) internal quality improvement, and (c) external transparency in patients with non-specific low back pain (NSLBP) in primary care physical therapy. METHODS: We used a consensus-driven modified RAND-UCLA Delphi method in seven steps with panellists (patients, representatives of patient and physiotherapy associations, researchers, policy makers, health insurers): (1) literature search, (2) first online survey, (3) patient interviews, (4) an experts meeting, (5) a consensus meeting, (6) second online survey, and (7) final approval of an advisory board. Steps 1-4 resulted in potential outcome measures. In the consensus meeting after discussion panellists voted for inclusion per measure. In the second online survey the final standard set was rated on relevance and feasibility on a 9-point Likert scale; when the median score was ≥ 7, the standard set was accepted and finally approved. RESULTS: Thirteen draft outcome measures were rated and discussed, and finally, six outcome measures were accepted. The standard set includes the Quebec Back Pain Disability Scale, Oswestry Disability Index, Patient-Specific Functional Scale, Numeric Pain Rating Scale, Global Perceived Effect (GPE-DV), and the STarT Back Screening Tool (SBT). CONCLUSION: This study presents a standard set of outcome measures for patients with NSLBP in primary care physiotherapy accepted for relevance and feasibility by stakeholders. The standard set is currently used in daily practice and tested on validity and reliability in a pilot study. These slides can be retrieved under Electronic Supplementary Material.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.228 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".